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Abstract Crop phenology regulates seasonal carbon and water fluxes between croplands and the atmosphere and provides essential information for monitoring and predicting crop growth dynamics and productivity. However, under rapid climate change and more frequent extreme events, future changes in crop phenological shifts have not been well investigated and fully considered in earth system modeling and regional climate assessments. Here, we propose an innovative approach combining remote sensing imagery and machine learning (ML) with climate and survey data to predict future crop phenological shifts across the US corn and soybean systems. Specifically, our projected findings demonstrate distinct acceleration patterns—under the RCP 4.5/RCP 8.5 scenarios, corn planting, silking, maturity, and harvesting stages would significantly advance by 0.94/1.66, 1.13/2.45, 0.89/2.68, and 1.04/2.16 days/decade during 2021–2099, respectively. Soybeans exhibit more muted responses with phenological stages showing relatively smaller negative trends (0.59, 1.08, 0.07, and 0.64 days/decade under the RCP 4.5 vs. 1.24, 1.53, 0.92, and 1.04 days/decade under the RCP 8.5). These spatially explicit projections illustrate how crop phenology would respond to future climate change, highlighting widespread and progressively earlier phenological timing. Based on these findings, we call for a specific effort to quantify the cascading effects of future phenology shifts on crop yield and carbon, water, and energy balances and, accordingly, craft targeted adaptive strategies. 

Abstract Conservation tillage has been promoted as an effective practice to preserve soil health and enhance agroecosystem services. Changes in tillage intensity have a profound impact on soil nitrogen cycling, yet their influence on nitrate losses at large spatiotemporal scales remains uncertain. This study examined the effects of tillage intensity on soil nitrate losses in the US Midwest from 1979–2018 using field data synthesis and process-based agroecosystem modeling approaches. Our results revealed that no-tillage (NT) or reduced tillage intensity (RTI) decreased nitrate runoff but increased nitrate leaching compared to conventional tillage. These trade-offs were largely caused by altered water fluxes, which elevated total nitrate losses. The structural equation model suggested that precipitation had more pronounced effects on nitrate leaching and runoff than soil properties (i.e. texture, pH, and bulk density). Reduction in nitrate runoff under NT or RTI was negatively correlated with precipitation, and the increased nitrate leaching was positively associated with soil bulk density. We further explored the combined effects of NT or RTI and winter cover crops and found that incorporating winter cover crops into NT systems effectively reduced nitrate runoff but did not significantly affect nitrate leaching. Our findings underscore the precautions of implementing NT or RTI to promote sustainable agriculture under changing climate conditions. This study provides valuable insights into the complex relationship between tillage intensity and nitrate loss pathways, contributing to informed decision-making in climate-smart agriculture. 

Abstract Biochar is well-accepted as a viable climate mitigation strategy to promote agricultural and environmental benefits such as soil carbon sequestration and crop productivity while reducing greenhouse gas emissions. However, its effects on soil microbial biomass carbon (SMBC) in field experiments have not yet been thoroughly explored. In this study, we collected 539 paired globally published observations to study the impacts of biochar on SMBC under field experiments. Our results suggested an overall positive impact of biochar (21.31%) on SMBC, varying widely with different climate conditions, soil types, biochar properties, and management practices. Biochar application exhibits significant impacts under climates with mean annual temperature (MAT) < 15 °C and mean annual precipitation (MAP) between 500 and 1000 mm. Soils of coarse and fine texture, alkaline pH (SPH), soil total organic carbon (STC) content up to 10 g/kg, soil total nitrogen (STN) content up to 1.5 g/kg, and low soil cation exchange capacity (SCEC) content of < 5 cmol/kg received higher positive effects of biochar application on SMBC. Biochar produced from crop residue, specifically from cotton and maize residue, at pyrolysis temperature (BTM) of < 400 °C, with a pH (BPH) between 8 and 9, low application rate (BAP) of < 10 t/ha, and high ash content (BASH) > 400 g/kg resulted in an increase in SMBC. Low biochar total carbon (BTC) and high total nitrogen (BTN) positively affect the SMBC. Repeated application significantly increased the SMBC by 50.11%, and fresh biochar in the soil (≤ 6 months) enhanced SMBC compared to the single application and aged biochar. Biochar applied with nitrogen fertilizer (up to 300 kg/ha) and manure/compost showed significant improvements in SMBC, but co-application with straw resulted in a slight negative impact on the SMBC. The best-fit gradient boosting machines model, which had the lowest root mean square error, demonstrated the relative importance of various factors on biochar effectiveness: biochar, soil, climate, and nitrogen applications at 46.2%, 38.1%, 8.3%, and 7.4%, respectively. Soil clay proportion, BAP, nitrogen application, and MAT were the most critical variables for biochar impacts on SMBC. The results showed that biochar efficiency varies significantly in different climatic conditions, soil environments, field management practices, biochar properties, and feedstock types. Our meta-analysis of field experiments provides the first quantitative review of biochar impacts on SMBC, demonstrating its potential for rehabilitating nutrient-deprived soils and promoting sustainable land management. To improve the efficiency of biochar amendment, we call for long-term field experiments to measure SMBC across diverse agroecosystems. Graphical Abstract 

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems. 

Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices.